Sensitivity Analysis of Dissolved Oxygen in Cold Region Rivers Through Numerical Modelling
Bibliographic record
Abstract
Dissolved oxygen (DO) is one of the most critical water quality constituents in cold region rivers. Harsh winter conditions pose significant challenges for DO sampling, making numerical modeling a valuable tool for gaining insights into DO concentrations during winter. Sensitivity analysis is essential for understanding the relative importance of the model parameters to the DO concentrations; however, such studies are rare. This study conducted a DO sensitivity analysis in the Lower Athabasca River, Canada, using a water quality model with ice effects in the MIKE HYDRO River. The simulated flow, water level, water temperature and DO concentrations closely matched observed values along the study reach. A bidirectional perturbation analysis was conducted to assess the sensitivity of DO concentrations to 14 model parameters. The results indicate that photosynthesis and respiration are the two most influential processes affecting river DO under winter conditions despite lower biomass activity compared to open-water conditions. A distinct seasonal pattern was observed for most parameters, with DO sensitivity during winter ice-covered periods being significantly higher than in summer open-water conditions. The study provides valuable insights for the development of integrated water quality and ice models for cold region rivers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".